Landslide surface scene deformation intelligent monitoring method and system based on rainfall forecast

Abstract

The invention relates to a rainfall forecast-based landslide surface scene deformation intelligent monitoring method and system. The method comprises the steps of determining a landslide rainfall risk level through multi-index coupling calculation; selecting and implementing a monitoring scheme based on the landslide rainfall risk level; the rainfall intensity is calculated according to the intelligent grid forecast data, a risk grade-rainfall intensity two-dimensional regulation and control matrix is established in combination with the landslide rainfall risk grade, and the risk grade and the rainfall intensity are mapped into regulation and control parameters in a cross mode, so that tracking dynamic adjustment of the monitoring frequency of deformation and rainfall is achieved; and constructing a rainfall-landslide meteorological risk index model, and when the rainfall-landslide meteorological risk index model exceeds a preset risk threshold, generating and issuing an early warning message. According to the invention, through combination of the multi-dimensional risk assessment model and the dynamic regulation and control matrix and integration of time-space coupling analysis, monitoring frequency adaptive optimization and multi-dimensional early warning linkage are realized, a landslide risk accurate perception-intelligent regulation and control-closed loop response full-chain prevention and control system is formed, and the active disaster prevention and control capability is significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of disaster monitoring, and in particular to an intelligent monitoring method and system for landslide surface scene deformation based on precipitation forecast. Background Art

[0003] At present, landslide monitoring mainly adopts a technical system that combines manual inspection with contact single-point monitoring. Since the scale of landslide bodies is usually tens of thousands of square meters, and there are differentiated deformation areas such as the front edge, back wall, and sliding surface, the existing technical solutions have three significant limitations: First, traditional GNSS receivers, crack meters and other equipment need to be densely deployed on the surface of the landslide body. Monitoring points, resulting in the procurement cost of single-point equipment accounting for more than 65% of the total investment, and there is a risk of personnel exposure during construction and maintenance; secondly, the existing system mostly uses discrete point data to extrapolate the overall deformation state. Due to the lack of spatial continuous monitoring means, when the distance between monitoring points exceeds 30 meters, the error rate of deformation field reconstruction will exceed 18%; finally, although the correlation model of precipitation monitoring and landslide early warning has been established, due to the lack of intelligent control capabilities of the monitoring system, it still maintains full-load operation during the non-rainfall period, resulting in a waste of power and communication resources. The measured data shows that the proportion of invalid data storage is as high as 42%.

[0004] It is worth noting that although the non-contact monitoring technologies such as InSAR and 3D laser scanning developed in recent years can realize surface monitoring, they are difficult to be applied on a large scale in the rainy mountainous areas of the southwest due to their high equipment costs (a single system costs more than 2 million yuan) and serious interference from clouds and fog (the effective data acquisition rate is less than 60%). Therefore, how to build an intelligent landslide monitoring system that takes into account both economy and safety while ensuring monitoring accuracy is still a technical problem that needs to be solved urgently in this field. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method and system for intelligently monitoring landslide surface deformation based on precipitation forecast, which solves the problems of high equipment deployment cost, poor spatial continuity, and lack of intelligent linkage with precipitation factors in the existing landslide monitoring technology, resulting in low monitoring efficiency and prominent safety hazards.

[0007] (II) Technical solution

[0008] In order to achieve the above object, the main technical solutions adopted by the present invention include:

[0009] In a first aspect, an embodiment of the present invention provides an intelligent monitoring method for landslide surface scene deformation based on precipitation forecast, comprising:

[0010] Based on the historical precipitation data of the area to be monitored, the landslide precipitation risk level is determined through the coupled calculation of the hazard factor risk index, the disaster-bearing environment risk index, and the vulnerability index of the disaster-affected body;

[0011] Based on the landslide precipitation risk level, adaptively select and implement a landslide surface deformation monitoring plan that includes one-dimensional deformation vector monitoring and / or three-dimensional deformation vector monitoring;

[0012] Calculate the precipitation intensity based on the obtained intelligent grid forecast data, combine it with the landslide precipitation risk level, establish a two-dimensional regulation matrix of risk level - precipitation intensity, and based on the two-dimensional regulation matrix mechanism, map the risk level and precipitation intensity to regulation parameters in real time to achieve the tracking and dynamic adjustment of the landslide surface deformation monitoring frequency and precipitation monitoring frequency;

[0013] Construct a precipitation-landslide meteorological risk index model based on the spatio-temporal coupling relationship between landslide deformation and cumulative rainfall. When the risk index value exceeds the preset risk threshold, generate and issue a warning message including geographical location information, landslide risk analysis, precipitation trend analysis, and emergency measures.

[0014] Optionally, determining the landslide precipitation risk level through the coupled calculation of the hazard factor risk index, the disaster-bearing environment risk index, and the vulnerability index of the disaster-affected body based on the historical precipitation data of the area to be monitored includes:

[0015] Obtain the historical meteorological observation data of the nearest N complete decades of the meteorological stations adjacent to the area to be monitored, and obtain the four-dimensional characteristic data including the maximum hourly rainfall, the maximum daily rainfall, the cumulative rainfall during the rainstorm process, and the number of days of rainstorm duration of the rainstorm process from the obtained historical meteorological observation data;

[0016] Normalize the four-dimensional characteristic data, and combine the weight coefficients of each four-dimensional characteristic data dynamically determined by using the information entropy weighting method, and obtain the hazard factor risk index of the area to be monitored by using the pre-constructed hazard factor risk index formula;

[0017] Use the adjacent unit altitude difference method to obtain the terrain undulation degree of the unit where the area to be monitored is located. Combine the slope and fault density of the unit where the area to be monitored is located. After normalization, obtain the disaster-bearing environment risk index of the area to be monitored by using the pre-constructed disaster-bearing environment risk index formula;

[0018] According to the gross domestic product, population density, and land use type of the unit where the area to be monitored is located, and use the information entropy weighting method to dynamically determine the weight coefficients of the gross domestic product, population density, and land use type, and obtain the vulnerability index of the disaster-affected body by using the pre-constructed vulnerability index formula of the disaster-affected body;

[0019] Based on the hazard factor risk index, disaster-bearing environment risk index, and hazard-exposed body vulnerability index, a multi-dimensional risk coupling calculation formula for hazard factor - disaster-bearing environment - hazard-exposed body is established, and the landslide precipitation risk degree of the area to be monitored is calculated;

[0020] Based on the percentile rank division of historical disaster statistics, a quantitative correspondence relationship between the landslide precipitation risk degree and the risk level is established;

[0021] Among them,

[0022] The formula for the hazard factor risk index is:

[0023]

[0024] In the formula, I is the hazard factor risk index, I 1max is the normalized value of the maximum hourly rainfall during the rainstorm process, I 24max is the normalized value of the maximum daily rainfall during the rainstorm process, I prc is the normalized value of the cumulative rainfall during the rainstorm process; I day is the normalized value of the number of days of the rainstorm process, n is the number of rainstorm processes in the past N whole decades, and a, b, c, and d are weight coefficients;

[0025] The formula for the disaster-bearing environment risk index is:

[0026] P = w 1 ×E + w 2 ×S + w 3 ×F;

[0027] In the formula, E is the terrain undulation degree of the unit where the area to be monitored is located, S is the terrain slope of the unit where the area to be monitored is located, F is the fault density of the unit where the area to be monitored is located, w 1 , w 2 , w 3 are the weight coefficients of the disaster-bearing environment factors;

[0028] The terrain undulation degree is:

[0029]

[0030] In the formula, h i is the altitude of the adjacent unit of the unit where the area to be monitored is located, is the altitude of the unit where the area to be monitored is located, n 0 is the number of adjacent units of the unit where the area to be monitored is located;

[0031] The formula for the hazard-exposed body vulnerability index is:

[0032] V = p 1 ×G + p 2 ×D + p 3 ×L;

[0033] Wherein, V is the vulnerability index of the disaster-bearing body, G is the normalized value of the gross domestic product of the unit where the area to be monitored is located, D is the normalized value of the population density of the unit where the area to be monitored is located, L is the land use type of the unit where the area to be monitored is located, p 1 、p 2 、p 3 are the weight coefficients of the disaster-bearing body factors;

[0034] The multi-dimensional risk coupling calculation formula of the disaster-causing factor - disaster-forming environment - disaster-bearing body is:

[0035] R = I × P × V;

[0036] Wherein, R is the landslide precipitation risk degree;

[0037] If the percentile of the landslide precipitation risk degree > 95%, the landslide precipitation risk level is the fourth risk level; if the percentile of the landslide precipitation risk degree > 80% and ≤ 95%, the landslide precipitation risk level is the third risk level; if the percentile of the landslide precipitation risk degree > 50% and ≤ 80%, the landslide precipitation risk level is the second risk level; if the percentile of the landslide precipitation risk degree ≤ 50%, the landslide precipitation risk level is the first risk level.

[0038] Optionally, the landslide surface scene deformation monitoring scheme that adaptively selects and implements one-dimensional deformation vector monitoring and / or three-dimensional deformation vector monitoring based on the landslide precipitation risk level includes:

[0039] Establish a risk level - monitoring mode mapping rule, and screen the determined risk level according to the risk level - monitoring mode mapping rule to obtain the corresponding monitoring mode;

[0040] Establish a selection matrix including detection dimension, field of view angle threshold, maximum detection distance device, and anti-electromagnetic interference level according to the determined monitoring mode;

[0041] Based on the device selection matrix and the risk level, dynamically match the device parameter combinations, and automatically trigger the device performance deployment condition when the risk level is increased to update and obtain the latest device selection scheme;

[0042] According to the landslide area, the maximum detection distance of the device, and the field of view angle of the device in the area to be monitored, combined with the introduced electromagnetic interference density compensation coefficient, obtain the minimum number of monitoring stations;

[0043] Based on the obtained minimum monitoring station data and the distribution of electromagnetic interference sources, introduce the line-of-sight rate factor to generate the optimal layout topology;

[0044] According to the acquired point cloud data, the radar beam obstruction area is identified, and the terrain obstruction index is obtained by dividing the area of ​​the obstruction area by the projected area of ​​the landslide area. When the terrain obstruction index is less than 0.3, the compensation mechanism is triggered: additional movable devices are added in the obstruction area to form redundant coverage with a monitoring radius overlap of ≥20%; the scanning frequency of adjacent nodes is dynamically increased to 1.5 times the baseline value;

[0045] When deploying precipitation monitoring stations in the area to be monitored, perform local rainfall correlation analysis, extract historical rainfall data of the monitoring stations and the landslide center point during the same period, and calculate the Pearson correlation coefficient ρ between the rainfall at the monitoring station and the landslide center point;

[0046] When ρ<0.95, the site relocation is triggered, and the optimization objective function of minimizing the absolute error of historical rainfall and the constraint condition that the distance between the site and the landslide edge is ≤1.2 times the landslide diameter is constructed. The center of the landslide is taken as the origin, and candidate grids with a radius of ≤1km are generated. Each candidate grid is traversed, and candidate grid points that meet the minimum value of the absolute error of historical rainfall and the distance from the landslide edge is ≤1.2 times the landslide diameter are selected to obtain the optimal layout coordinates.

[0047] After the precipitation monitoring station is deployed, the correlation coefficient change is monitored in real time. If the Pearson correlation coefficient ρ of N consecutive rainfall events decreases by more than 5%, the station relocation step is triggered again;

[0048] in,

[0049] The risk level-monitoring mode mapping rule is as follows: when the risk level is the first risk level, the one-dimensional deformation vector monitoring mode implemented by the space-borne SAR or slope radar is adopted; when the risk level is the second risk level, the one-dimensional deformation vector monitoring mode is superimposed on the intermittent three-dimensional deformation vector monitoring mode implemented by the Beidou radar; when the risk level is the third risk level, the three-dimensional deformation vector monitoring mode implemented by the Beidou radar is superimposed on the intermittent one-dimensional deformation vector monitoring mode; when the risk level is the fourth risk level, the dual-mode full-band collaborative monitoring mode of the one-dimensional deformation vector monitoring mode and the three-dimensional deformation vector monitoring mode is adopted, and the dual-mode data acquisition time synchronization error is ≤1 second;

[0050] The conditions for equipment performance upgrade are:

[0051]

[0052] Where D current is the actual detection distance of the current equipment, L is the quantitative value of the current landslide risk level, 1 to 4 correspond to the first risk level to the fourth risk level, L 0 is the base risk level, the base risk level value is equal to the first risk level quantitative value, D baseTake the basic detection distance, with the value being the major axis length of the landslide area × 1.2;

[0053] The minimum monitoring station data is:

[0054]

[0055] In the formula, S is the projected area of the landslide area, D is the maximum detection distance of the device, θ is the field of view angle, α is the electromagnetic interference density compensation coefficient, EMI d is the electromagnetic interference density index, n r is the electromagnetic interference source data, r j is the average radiation power.

[0056] Optionally, calculate the precipitation intensity according to the obtained intelligent grid forecast data, combine with the landslide precipitation risk level, establish a two-dimensional control matrix of risk level - precipitation intensity, and based on the two-dimensional control matrix mechanism, cross-map the risk level and precipitation intensity into control parameters in real time to achieve the tracking dynamic adjustment of the deformation monitoring frequency and precipitation monitoring frequency of the landslide surface scenario, including:

[0057] Construct a two-dimensional control matrix of landslide precipitation risk level - precipitation intensity, set the landslide precipitation risk level as the horizontal control parameter of the matrix, and the precipitation intensity level as the vertical control parameter;

[0058] Preset the deformation monitoring frequency threshold and precipitation monitoring frequency threshold at each cross-node of the two-dimensional control matrix to form a standardized control parameter mapping rule;

[0059] Obtain the intelligent grid precipitation forecast data of the area where the landslide is located in real time, calculate the precipitation intensity index according to the intelligent grid precipitation forecast data and divide the dynamic precipitation intensity level;

[0060] Input the obtained dynamic precipitation intensity level of the area to be monitored and the landslide precipitation risk level into the two-dimensional control matrix for cross-map matching, and output the adapted deformation monitoring frequency control parameter and precipitation monitoring frequency control parameter;

[0061] According to the control parameters, remotely configure the working mode of the automated monitoring equipment, perform one-dimensional / three-dimensional deformation vector acquisition according to the deformation monitoring frequency threshold of the landslide scenario, and keep the sampling frequency of the rain gauge monitoring equipment not lower than the deformation monitoring frequency and dynamically match the change of precipitation intensity;

[0062] Establish a multi-dimensional data linkage trigger mechanism, and trigger the recalibration of control parameters when the following situations occur: the actual precipitation intensity breaks through the forecast level threshold, the deformation rate exceeds the preset safety threshold, and the continuous number of days of the rainstorm process reaches the preset critical value of the matrix;

[0063] After the precipitation process ends, start the ramp-down regulation, reduce the monitoring frequency in gradients, and simultaneously update the weights of historical event parameters in the two-dimensional regulation matrix.

[0064] Optionally, obtain the intelligent grid precipitation forecast data of the landslide area in real time, and calculate the precipitation intensity index based on the intelligent grid precipitation forecast data and divide the dynamic precipitation intensity levels, including:

[0065] Obtain the intelligent grid forecast data, extract the meteorological element information including the start time, end time, and hourly rainfall of the future precipitation process based on the longitude and latitude information of the central position of the landslide area, and calculate the maximum hourly rainfall, maximum daily rainfall, cumulative rainfall, and duration days of the rainstorm process based on the meteorological element information;

[0066] According to the normalized values of the maximum hourly rainfall, maximum daily rainfall, cumulative rainfall, and duration days of the rainstorm process, combined with the number of rainstorm processes and corresponding weight coefficients in the recent N whole decades, calculate the precipitation intensity index of the future precipitation process;

[0067] Adopt the historical meteorological observation data of the adjacent meteorological stations in the area to be monitored in the recent N whole decades, and establish a precipitation intensity classification table with multiple levels by calculating the precipitation intensity index of each rainstorm process.

[0068] Optionally, construct a precipitation-landslide meteorological risk index model based on the spatio-temporal coupling relationship between the landslide deformation and the cumulative rainfall. When the risk index value exceeds the preset risk threshold, generate and publish a warning message including geographical location information, landslide risk analysis, precipitation trend analysis, and emergency measures, including:

[0069] In the landslide initiation stage, divide the reference calculation unit based on the sampling frequency of the monitoring equipment;

[0070] Synchronously collect and align the three-dimensional deformation vector increment of the landslide surface and the corresponding cumulative rainfall within each time period to form a spatio-temporal coupling data set;

[0071] According to the reference calculation unit, use the non-linear weighted algorithm to calculate the precipitation-landslide meteorological risk index. At the same time, when the deformation rate exceeds 30% of the reference value, automatically shorten the time period length of the reference calculation unit;

[0072] Set different risk threshold intervals based on the clustering analysis results of historical disaster data;

[0073] When the precipitation-landslide meteorological risk index calculated in real time exceeds the corresponding risk threshold interval, automatically associate the three-dimensional geographical coordinates and influence radius of the landslide body, the contour map of the real-time deformation field, the intelligent grid precipitation forecast product for the next 72 hours, and the emergency plan from at least one data source, and package them into a structured warning message;

[0074] Push the structured early warning message to the preset processing entity synchronously through the satellite communication channel, 5G emergency broadcast, and Internet of Things terminals.

[0075] In a second aspect, an embodiment of the present invention provides a landslide surface scenario deformation intelligent regulation and monitoring system based on precipitation forecasting, including:

[0076] A landslide automatic monitoring station cluster, including a one-dimensional deformation monitoring station, a three-dimensional deformation monitoring station, and a rainfall monitoring station;

[0077] A landslide monitoring basic information database for storing the original data, intermediate data, and analysis result data related to the landslide surface scenario deformation integrated monitoring system, including meteorological data, geographic information data, geological environment data, social and economic data, landslide monitoring data, early warning information data, intelligent grid forecast data, rainfall historical data, precipitation monitoring data, landslide deformation monitoring data, and equipment information; and,

[0078] An intelligent regulation and management platform for executing the method as described above. The intelligent regulation and management platform also interacts with the intelligent grid forecast system of the meteorological department.

[0079] Optionally,

[0080] The one-dimensional deformation monitoring station is configured with spaceborne SAR or slope radar;

[0081] The three-dimensional deformation monitoring station is configured with Beidou surface scenario radar;

[0082] The rainfall monitoring station is deployed within 1 km of the area to be monitored.

[0083] Optionally, the landslide monitoring basic information database includes the following interrelated sub-databases:

[0084] A meteorological data sub-database storing meteorological data including precipitation observation historical data and intelligent grid weather forecast precipitation data for the past N whole decades in the administrative region where it is located;

[0085] A geographic information data sub-database storing geographic data including altitude, slope, and land use data in the administrative region where it is located;

[0086] A geological environment data sub-database storing geological environment data including formation lithology and fault density data in the administrative region where it is located;

[0087] A social and economic data sub-database storing social and economic data including gross domestic product and population density data in the administrative region where it is located;

[0088] A landslide monitoring data sub-database storing relevant monitoring data including landslide surface scenario deformation monitoring data, precipitation observation data, and performance parameters and operating status of automatic monitoring equipment during the monitoring period in the area to be monitored;

[0089] The early warning message data sub-library stores early warning information including geographical location information, landslide risk analysis, precipitation trend analysis, and emergency measures.

[0090] Optionally, it includes: a display terminal, communicatively connected to the intelligent regulation and management platform, used for logging in to the landslide monitoring management platform, and local display of data including landslide monitoring data, precipitation forecast, and precipitation-landslide meteorological risk analysis results.

[0091] (III) Beneficial effects

[0092] The beneficial effects of the present invention are as follows:

[0093] First, by fusing historical precipitation data and geological environment parameters to construct a multi-factor risk assessment system, the present invention breaks through the limitations of traditional single meteorological thresholds, reasonably controls the spatio-temporal resolution accuracy of landslide risk levels, and provides a scientific decision-making basis for differential monitoring resource allocation.

[0094] Second, through the elastic configuration mechanism of adaptively selecting one-dimensional / three-dimensional deformation monitoring schemes, it can automatically optimize the monitoring resource allocation for different risk scenarios, reduce equipment redundancy, and reduce equipment energy consumption while ensuring data integrity.

[0095] Furthermore, an innovative two-dimensional regulation matrix of risk level - precipitation intensity is established, and dynamic monitoring frequency instructions are generated through double-parameter cross-mapping, enabling the monitoring system to have an intelligent learning ability, ensuring that the monitoring sensitivity synchronously responds to the disaster evolution process, realizing the intelligent tracking adjustment of the monitoring frequency, and overcoming the lag defect of fixed-frequency monitoring in the heavy rain mutation scenario.

[0096] Finally, based on the meteorological risk index model constructed by the spatio-temporal coupling relationship, the deformation rate and rainfall cumulative effect are deeply fused and multi-scale correlation analysis is carried out. Through the multi-modal early warning message generation mechanism, it ensures the rapid positioning, accurate parsing, and multi-channel synchronous issuance of emergency response instructions, forms a closed-loop management chain from risk perception to decision execution, greatly improves the active prevention and control efficiency of landslide disasters, and reduces risks. Description of the drawings

[0097] Figure 1 It is a schematic flow chart of the method provided by the embodiment of the present invention;

[0098] Figure 2 It is a specific schematic flow chart of step S1 of the method provided by the embodiment of the present invention;

[0099] Figure 3 It is a specific schematic flow chart of step S2 of the method provided by the embodiment of the present invention;

[0100] Figure 4Schematic diagram of the specific process of step S3 of the method provided by the embodiment of the present invention;

[0101] Figure 5 Schematic diagram of the specific process of step S33 of the method provided by the embodiment of the present invention

[0102] Figure 6 Schematic diagram of the specific process of step S4 of the method provided by the embodiment of the present invention. Detailed implementation manner

[0103] To better explain the present invention for easy understanding, the present invention will be described in detail below in conjunction with the accompanying drawings through specific implementation manners.

[0104] As Figure 1 shown, an intelligent monitoring method for landslide surface scene deformation based on precipitation forecast proposed by the embodiment of the present invention includes: determining the landslide precipitation risk level through the coupling calculation of the hazard factor risk index, the disaster-bearing environment risk index, and the disaster-affected body vulnerability index according to the historical precipitation data of the area to be monitored obtained; adaptively selecting and implementing a landslide surface scene deformation monitoring scheme including one-dimensional deformation vector monitoring and / or three-dimensional deformation vector monitoring based on the landslide precipitation risk level; calculating the precipitation intensity according to the obtained intelligent grid forecast data, combining with the landslide precipitation risk level, establishing a two-dimensional regulation matrix of risk level - precipitation intensity, and based on the two-dimensional regulation matrix mechanism, mapping the risk level and precipitation intensity into regulation parameters in real time to realize the tracking dynamic adjustment of the deformation monitoring frequency and precipitation monitoring frequency of the landslide surface scene; constructing a precipitation - landslide meteorological risk index model based on the spatio-temporal coupling relationship between the landslide deformation amount and the cumulative rainfall amount, and when the risk index value exceeds the preset risk threshold, generating and issuing a warning message including geographical location information, landslide risk analysis, precipitation trend analysis, and emergency measures.

[0105] First of all, the present invention constructs a multi-factor risk assessment system by integrating historical precipitation data and geological environment parameters, breaks through the limitations of traditional single meteorological thresholds, reasonably controls the spatio-temporal resolution accuracy of the landslide risk level, and provides a scientific decision-making basis for differential monitoring resource allocation.

[0106] Secondly, through the elastic configuration mechanism of adaptively selecting one-dimensional / three-dimensional deformation monitoring schemes, it can automatically optimize the monitoring resource allocation for different risk scenarios, reduce equipment redundancy, and reduce equipment energy consumption while ensuring data integrity.

[0107] Furthermore, an innovative two-dimensional regulation matrix of risk level - precipitation intensity is established, and dynamic monitoring frequency instructions are generated through two-parameter cross mapping, enabling the monitoring system to have an intelligent learning ability, ensuring that the monitoring sensitivity keeps synchronous response with the disaster evolution process, realizing the intelligent tracking adjustment of the monitoring frequency, and overcoming the lag defect of fixed-frequency monitoring in the sudden rainstorm scenario.

[0108] Finally, the meteorological risk index model constructed based on the spatio-temporal coupling relationship deeply integrates the deformation rate and the cumulative effect of rainfall and conducts multi-scale correlation analysis. Through the multi-modal early warning message generation mechanism, it ensures the rapid positioning, accurate parsing, and multi-channel synchronous issuance of emergency response instructions, forming a closed-loop management chain from risk perception to decision execution, greatly improving the active prevention and control efficiency of landslide disasters and reducing risks.

[0109] To better understand the above technical solution, the exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more clear and thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0110] Specifically, the embodiment of the present invention provides an intelligent monitoring method for landslide surface deformation based on precipitation forecasting, including:

[0111] S1. According to the historical precipitation data of the area to be monitored obtained, determine the landslide precipitation risk level through the coupled calculation of the hazard index of the disaster-causing factor, the hazard index of the disaster-bearing environment, and the vulnerability index of the disaster-affected body.

[0112] Further, as Figure 2 shown, step S1 includes:

[0113] S11. Obtain the historical meteorological observation data of the adjacent meteorological stations in the area to be monitored for the recent N complete decades, and obtain the four-dimensional characteristic data including the maximum hourly rainfall, the maximum daily rainfall, the cumulative rainfall, and the duration of the rainstorm process for the recent N complete decades from the obtained historical meteorological observation data.

[0114] It should be clear that the rainstorm process is: the whole process from the start to the end of rainfall affected by the weather system is a rainfall process. During the rainfall process, when the rainfall meets the condition that the 24-hour precipitation is greater than or equal to 50.0 mm, or the 12-hour precipitation is greater than or equal to 30.0 mm, it is a rainstorm process.

[0115] S12. Perform normalization processing on the four-dimensional characteristic data, and combine the weight coefficients of each four-dimensional characteristic data dynamically determined by using the information entropy weighting method, and obtain the hazard index of the disaster-causing factor of the area to be monitored as follows through the pre-constructed hazard index formula of the disaster-causing factor:

[0116]

[0117] In the formula, I is the hazard index of the disaster-causing factor, I 1maxThe normalized value of the maximum hourly rainfall during a rainstorm process, I 24max The normalized value of the maximum daily rainfall during a rainstorm process, I prc The normalized value of the cumulative rainfall during a rainstorm process; I day The normalized value of the number of days of the rainstorm process, n is the number of rainstorm processes in the recent N complete decades, generally not less than 3, and a, b, c, and d are weight coefficients.

[0118] S13. The topographic undulation degree of the unit where the area to be monitored is located is obtained by using the altitude difference method of adjacent units. Combining the slope and fault density of the unit where the area to be monitored is located, after normalization, the following hazard index of the disaster-bearing environment of the area to be monitored is obtained through the pre-constructed hazard index formula of the disaster-bearing environment:

[0119] P = w 1 ×E + w 2 ×S + w 3 ×F;

[0120] In the formula, E is the normalized value of the topographic undulation degree of the unit where the area to be monitored is located, S is the topographic slope of the unit where the area to be monitored is located, F is the fault density of the unit where the area to be monitored is located, w 1 、w 2 、w 3 are the weight coefficients of the disaster-bearing environment factors.

[0121] The topographic undulation degree is:

[0122]

[0123] In the formula, h i is the altitude of the adjacent unit of the unit where the area to be monitored is located, is the altitude of the unit where the area to be monitored is located, n 0 is the number of adjacent units of the unit where the area to be monitored is located.

[0124] S14. According to the obtained gross domestic product, population density, and land use type of the unit where the area to be monitored is located, and using the information entropy weight method to dynamically determine the weight coefficients of the gross domestic product, population density, and land use type, the following vulnerability index of the disaster-bearing body is obtained through the pre-constructed vulnerability index formula of the disaster-bearing body:

[0125] V = p 1 ×G + p 2 ×D + p 3 ×L;

[0126] Wherein, V is the vulnerability index of the disaster-bearing body, G is the normalized value of the GDP of the unit where the area to be monitored is located, D is the normalized value of the population density of the unit where the area to be monitored is located, L is the land use type of the unit where the area to be monitored is located, and the land use type is divided into six categories: urban and rural industrial and mining residential land, cultivated land, forest land, grassland, water area, and unused land. L is respectively assigned values of 1, 0.8, 0.6, 0.4, 0.2, and 0, p 1 、p 2 、p 3 are the weight coefficients of the disaster-bearing body factors.

[0127] S15. Based on the hazard index of the disaster-causing factor, the hazard index of the disaster-bearing environment, and the vulnerability index of the disaster-bearing body, establish a multi-dimensional risk coupling calculation formula for the disaster-causing factor - disaster-bearing environment - disaster-bearing body, and calculate the landslide precipitation risk degree of the area to be monitored. Among them, the multi-dimensional risk coupling calculation formula for the disaster-causing factor - disaster-bearing environment - disaster-bearing body is:

[0128] R = I × P × V;

[0129] Wherein, R is the landslide precipitation risk degree.

[0130] Then, calculate the landslide precipitation risk degree of the historical landslide events induced by precipitation in the administrative region (province or city) where the landslide area to be monitored is located in the past 10 years, and use the percentile method to divide the landslide precipitation risk level. Specifically, when the percentile of the landslide precipitation risk degree > 95%, the landslide precipitation risk level is the fourth risk level; when the percentile of the landslide precipitation risk degree > 80% and ≤ 95%, the landslide precipitation risk level is the third risk level; when the percentile of the landslide precipitation risk degree > 50% and ≤ 80%, the landslide precipitation risk level is the second risk level; when the percentile of the landslide precipitation risk degree ≤ 50%, the landslide precipitation risk level is the first risk level.

[0131] S2. Based on the landslide precipitation risk level, adaptively select and implement a landslide surface scene deformation monitoring scheme including one-dimensional deformation vector monitoring and / or three-dimensional deformation vector monitoring.

[0132] Furthermore, as Figure 3 shown, step S2 includes:

[0133] S21. Establish a risk level - monitoring mode mapping rule, and screen the determined risk level according to the risk level - monitoring mode mapping rule to obtain the corresponding monitoring mode.

[0134] Specifically, the risk level - monitoring mode mapping rules are as follows: When the risk level is the first risk level, a one - dimensional deformation vector monitoring mode implemented by spaceborne SAR or slope radar is adopted; when the risk level is the second risk level, a three - dimensional deformation vector monitoring mode implemented by Beidou radar in an intermittent manner is superimposed on the basis of the one - dimensional deformation vector monitoring mode; when the risk level is the third risk level, a three - dimensional deformation vector monitoring mode implemented by Beidou radar is superimposed with an intermittent one - dimensional deformation vector monitoring mode; when the risk level is the fourth risk level, a dual - mode full - band collaborative monitoring mode of one - dimensional deformation vector monitoring mode and three - dimensional deformation vector monitoring mode is adopted, and the synchronization error of dual - mode data acquisition time is ≤1 second.

[0135] S22. Establish a selection matrix including detection dimension, field - of - view angle threshold, maximum detection distance device, and anti - electromagnetic interference level according to the determined monitoring mode.

[0136] In this step, establish an equipment selection matrix, and the matrix dimensions include: detection dimension (one - dimensional / three - dimensional), field - of - view angle threshold (horizontal field - of - view angle 50°, vertical field - of - view angle 25°), maximum detection distance (≥1.2 times the diagonal length of the landslide area), and anti - electromagnetic interference level (≥IP67).

[0137] Dynamically match the equipment parameter combinations according to the risk level. When the risk level is increased, the following equipment performance upgrade conditions are automatically triggered, so that the equipment selection is dynamically upgraded with the risk level, avoiding performance redundancy or deficiency in traditional experience - based selection:

[0138]

[0139] In the formula, D current is the actual detection distance of the current equipment, obtained through equipment performance calibration, L is the quantization value of the current landslide risk level, 1 - 4 respectively correspond to the first risk level to the fourth risk level, L 0 is the reference risk level, and the reference risk level value is equal to the quantization value of the first risk level, D base is the basic detection distance, and its value is the long - axis length of the landslide area ×1.2.

[0140] S23. Based on the equipment selection matrix and the risk level, dynamically match the equipment parameter combinations, and when the risk level is increased, automatically trigger the equipment performance allocation conditions to update and obtain the latest equipment selection plan.

[0141] S24. According to the landslide area of the area to be monitored, the maximum detection distance of the equipment, and the field - of - view angle of the equipment, combined with the introduced electromagnetic interference density compensation coefficient, obtain the following minimum number of monitoring stations:

[0142]

[0143] Where ceil is the mathematical function rounded upward, S is the projected area of ​​the landslide area, calculated by GIS geo-fencing, D is the maximum detection distance of the device, θ is the field of view angle, which refers to the horizontal coverage angle of the radar beam, α is the electromagnetic interference density compensation coefficient (0.2-0.5), EMI d is the electromagnetic interference density index, n r is the electromagnetic interference source data, r j is the average radiation power, η is the visibility factor, and the visible area ratio is calculated using the digital elevation model (DEM).

[0144] S25. Based on the obtained minimum monitoring station data and the distribution of electromagnetic interference sources, the visibility factor is introduced to generate the optimal deployment topology.

[0145] It should be understood that when generating the layout topology map, the visibility factor η (≥85%) is introduced. When the regional visibility is insufficient, the density of slope monitoring equipment is automatically increased to

[0146]

[0147] S26, identifying the radar beam blocking area based on the acquired point cloud data, and dividing the area of ​​the blocking area by the projected area of ​​the landslide area to obtain the terrain blocking index When the terrain obstruction index is less than 0.3, the compensation mechanism is triggered: additional movable devices are added in the shielded area to form redundant coverage with a monitoring radius overlap of ≥ 20%; the scanning frequency of adjacent nodes is dynamically increased to 1.5 times the baseline value.

[0148] S27. When deploying precipitation monitoring stations in the area to be monitored, perform local rainfall correlation analysis, extract historical rainfall data of the monitoring stations and the landslide center point during the same period, and calculate the Pearson correlation coefficient ρ between the rainfall at the monitoring station and the landslide center point.

[0149] The location of the precipitation monitoring station shall be determined according to the scope of the landslide area to be monitored. The number of precipitation monitoring stations to be set is 1, which should be set within 1km of the monitoring area. The location of the stations should ensure that the observation data accurately reflects the dynamic changes in rainfall in the monitoring area.

[0150] S28. When ρ<0.95, the site relocation is triggered, and the optimization objective function that minimizes the absolute error of historical rainfall and the constraint condition that the distance between the site and the landslide edge is ≤1.2 times the landslide diameter is constructed. Taking the center of the landslide as the origin, a candidate grid with a radius of ≤1km is generated. Each candidate grid is traversed, and the candidate grid points that meet the minimum value of the absolute error of historical rainfall and the distance to the landslide edge ≤1.2 times the landslide diameter are selected to obtain the optimal layout coordinates.

[0151] Among them, the optimization objective function is:

[0152]

[0153] Wherein, R s (t) - R l (t) is the absolute deviation of rainfall per unit time unit. The cumulative historical total deviation reflects the representativeness error of the point. Here, the objective function of minimizing the cumulative absolute deviation is adopted, effectively suppressing the representation error of extreme rainfall events.

[0154] S29. Monitor the change of the correlation coefficient in real time after the precipitation monitoring station is deployed. If the Pearson correlation coefficient ρ of N consecutive rainfall events drops by more than 5%, trigger the steps of relocating the station again. By setting a strict threshold of ρ≥0.95, ensure the high consistency between the rainfall data of the monitoring station and the actual rainfall at the landslide point.

[0155] S3. Calculate the precipitation intensity according to the obtained intelligent grid forecast data, combine with the landslide precipitation risk level, establish a two-dimensional regulation matrix of risk level - precipitation intensity, and based on the two-dimensional regulation matrix mechanism, map the risk level and precipitation intensity to regulation parameters in real time to realize the tracking dynamic adjustment of the deformation monitoring frequency and precipitation monitoring frequency of the landslide surface scenario.

[0156] Adopt the historical meteorological observation data of nearly N whole decades of the meteorological station adjacent to the landslide area to be monitored, calculate the precipitation intensity index of each rainstorm process, and use the percentile method to divide the precipitation intensity level. The precipitation intensity level is divided into 5 levels in total. When the percentile of the precipitation intensity index > 95%, the precipitation intensity level is the extremely strong level; when the percentile of the precipitation intensity index > 85% and ≤ 95%, the precipitation intensity level is the very strong level; when the percentile of the precipitation intensity index > 70% and ≤ 85%, the precipitation intensity level is the strong level; when the percentile of the precipitation intensity index > 50% and ≤ 70%, the precipitation intensity level is the relatively strong level; when the percentile of the precipitation intensity index ≤ 50%, the precipitation intensity level is the general level.

[0157] Furthermore, as Figure 4 shown, step S3 includes:

[0158] S31. Construct a two-dimensional regulation matrix of landslide precipitation risk level - precipitation intensity, set the landslide precipitation risk level as the horizontal regulation parameter of the matrix, and set the precipitation intensity level as the vertical regulation parameter.

[0159] The horizontal axis defines the landslide precipitation risk level from the first level (low risk) to the fourth level (extremely high risk); the vertical axis sets the precipitation intensity level from general (level 1) to extremely strong (level 5), configured according to the intelligent grid forecast division standard; the deformation monitoring frequency parameters are preset in the matrix cells (for example, when the first-level risk + level 1 precipitation, it is set to once every 2 hours, and when the fourth-level risk + level 5 precipitation, it is set to once every 10 minutes). In this way, a 20-node regulation matrix is formed.

[0160] Two types of monitoring parameters are preset in each matrix node:

[0161] Deformation monitoring frequency threshold: Set according to the combination of risk level and precipitation intensity (for example, high risk + extremely strong precipitation corresponds to once every 30 minutes);

[0162] Precipitation monitoring frequency threshold: Always set to 1.2 times the deformation monitoring frequency (for example, if the deformation is once every 30 minutes, then the precipitation is once every 25 minutes).

[0163] S32. Preset the deformation monitoring frequency threshold of the landslide scenario and the precipitation monitoring frequency threshold at each cross-node of the two-dimensional regulation matrix to form a standardized regulation parameter mapping rule.

[0164] Input the real-time obtained landslide area risk level and dynamic precipitation intensity level into the regulation matrix, automatically match the node coordinates, and output the deformation monitoring frequency parameter (such as once every 30 minutes) and precipitation monitoring frequency parameter (such as once every 25 minutes).

[0165] S33. Obtain the intelligent grid precipitation forecast data of the area where the landslide is located in real time, and calculate the precipitation intensity index according to the intelligent grid precipitation forecast data and divide the dynamic precipitation intensity level.

[0166] Furthermore, as Figure 5 shown, step S33 includes:

[0167] S331. Obtain the intelligent grid forecast data, according to the longitude and latitude information of the central position of the area where the landslide is located, extract the meteorological element information including the start time, end time, and hourly rainfall of the future precipitation process, and calculate the maximum hourly rainfall, maximum daily rainfall, cumulative rainfall, and the duration days of the rainstorm process based on the meteorological element information.

[0168] S332. According to the normalized values of the maximum hourly rainfall, maximum daily rainfall, cumulative rainfall, and the duration days of the rainstorm process, combined with the number of rainstorm processes and corresponding weight coefficients in the recent N complete decades, calculate the precipitation intensity index of the future precipitation process.

[0169] S333. Adopt the historical meteorological observation data of the neighboring meteorological stations in the area to be monitored in the recent N complete decades, and establish a precipitation intensity classification table with multiple levels by calculating the precipitation intensity index of each rainstorm process.

[0170] In one embodiment, according to the longitude and latitude information of the central position of the landslide area, meteorological element information such as the start time, end time, and hourly rainfall of the future precipitation process is obtained from the intelligent grid forecast, and the maximum hourly rainfall, maximum daily rainfall, cumulative rainfall, and the duration days of the rainstorm process during the precipitation process are calculated.

[0171] Calculate the precipitation intensity index of the future precipitation process.

[0172] M = a×M 1max +b×M 24max +c×M prc +d×M day ;

[0173] In the formula, M is the precipitation intensity index, M 1max is the normalized value of the maximum hourly rainfall during the precipitation process; M 24max is the normalized value of the maximum daily rainfall during the precipitation process, M prc is the normalized value of the cumulative rainfall during the precipitation process; M day is the normalized value of the duration days of the precipitation process, n is the number of rainstorm processes in the recent N complete decades. a, b, c, and d are weight coefficients.

[0174] S34. Input the obtained dynamic precipitation intensity level and landslide precipitation risk level of the area to be monitored into the two-dimensional regulation matrix for cross-mapping and matching, and output the adapted deformation monitoring frequency regulation parameters and precipitation monitoring frequency regulation parameters.

[0175] S35. Remotely configure the working mode of the automated monitoring equipment according to the regulation parameters, perform one-dimensional / three-dimensional deformation vector acquisition according to the deformation monitoring frequency threshold of the landslide scenario, and keep the sampling frequency of the rainfall monitoring equipment not lower than the deformation monitoring frequency and dynamically match the change of precipitation intensity.

[0176] S36. Establish a multi-dimensional data linkage trigger mechanism, and trigger the re-calibration of the regulation parameters when the following situations occur: the actual precipitation intensity breaks through the forecast level threshold, the deformation rate exceeds the preset safety threshold, and the duration days of the rainstorm process reach the preset critical value of the matrix.

[0177] Specifically, deploy edge computing nodes to compare the deviation rate of measured rainfall - forecast rainfall in real time. When the deviation ≥ 25%, trigger the re-calibration signal; the deformation data stream is connected to the early warning bus, and the trigger threshold is set to a continuous 3-cycle growth rate exceeding 15%; the rainstorm duration days counter is linked with the preset parameters of the matrix, and activate the standby monitoring channel after reaching the critical value.

[0178] S37. Start the back-slope regulation after the precipitation process ends, reduce the monitoring frequency in gradients, and synchronously update the historical event parameter weights in the two-dimensional regulation matrix.

[0179] Start the 5-day countdown ramp-down process after precipitation ends: maintain a 50% monitoring frequency on the first day, decreasing by 10% each day. For the update of historical event parameter weights, the exponential smoothing algorithm is used, giving priority to enhancing the impact factor of recent abnormal events (e.g., the weight ratio of the last 3 events is 60%).

[0180] S4. Construct a precipitation-landslide meteorological risk index model based on the spatio-temporal coupling relationship between landslide deformation and cumulative rainfall. When the risk index value exceeds the preset risk threshold, generate and issue a warning message containing geographical location information, landslide risk analysis, precipitation trend analysis, and emergency measures.

[0181] Furthermore, as Figure 6 shown, step S4 includes:

[0182] S41. In the landslide initiation stage, divide the benchmark calculation unit based on the sampling frequency of the monitoring equipment.

[0183] S42. Synchronously collect and align the three-dimensional deformation vector increment of the landslide surface and the corresponding cumulative rainfall within each time period to form a spatio-temporal coupling data set.

[0184] S43. According to the benchmark calculation unit, use the non-linear weighted algorithm to calculate the precipitation-landslide meteorological risk index. At the same time, when the deformation rate exceeds 30% of the benchmark value, automatically shorten the time period length of the benchmark calculation unit.

[0185] S44. Set different risk threshold intervals based on the clustering analysis results of historical disaster data.

[0186] S45. When the precipitation-landslide meteorological risk index calculated in real-time exceeds the corresponding risk threshold interval, automatically associate the three-dimensional geographical coordinates and influence radius of the landslide body, the contour map of the real-time deformation field, the intelligent grid precipitation forecast product for the next 72 hours, and the emergency plan from at least one data source, and encapsulate them into a structured warning message.

[0187] S46. Push the structured warning message to the preset processing entity through satellite communication channels, 5G emergency broadcasts, and Internet of Things terminals.

[0188] When a landslide occurs, based on precipitation observation data and landslide surface deformation monitoring data, taking the time interval of the monitoring frequency as the unit time, calculate the precipitation-landslide meteorological risk index. The calculation formula is as follows:

[0189]

[0190] In the formula, K is the precipitation-landslide meteorological risk index, △d i is the landslide surface deformation amount within the i-th unit time when the landslide starts to occur, △r i is the cumulative rainfall within the i-th unit time when the landslide starts to occur. n1 is the number of time periods since the landslide started to occur.

[0191] Compare the calculated result with a preset precipitation-landslide meteorological risk threshold. If it exceeds the threshold and reaches the warning condition, generate a warning message and send the warning message in a predetermined manner. Among them, the warning message should at least include the geographical location information of the precipitation-landslide meteorological risk warning, the landslide risk situation, the future precipitation situation, and the countermeasures to be taken. Otherwise, continue to monitor the landslide area to be monitored.

[0192] In addition, an embodiment of the present invention provides a landslide surface scene deformation intelligent regulation and monitoring system based on precipitation forecasting, including:

[0193] A cluster of landslide automatic monitoring stations, including one-dimensional deformation monitoring stations, three-dimensional deformation monitoring stations, and rainfall monitoring stations.

[0194] A landslide monitoring basic information database for storing the original data, intermediate data, and analysis result data related to the landslide surface scene deformation integrated monitoring system, including meteorological data, geographical information data, geological environment data, social and economic data, landslide monitoring data, warning information data, intelligent grid forecast data, rainfall historical data, precipitation monitoring data, landslide deformation monitoring data, and equipment information; and,

[0195] An intelligent regulation and management platform for executing the method as described above. The intelligent regulation and management platform also interacts with the intelligent grid forecast system of the meteorological department.

[0196] For further understanding, the one-dimensional deformation monitoring station: Deploy spaceborne SAR (Synthetic Aperture Radar) or slope radar, arrange it along the main sliding direction of the landslide body, and monitor the displacement along the radar line of sight in real time, with a maximum allowable error of ±20mm; the three-dimensional deformation monitoring station: Configure a Beidou surface scene radar to monitor the three-dimensional deformation displacement of the landslide body in real time, with a maximum allowable error of ±15mm in the horizontal direction and ±20mm in the vertical direction; the rainfall monitoring station: Install a high-precision tipping bucket or weighing type rain gauge within 1km of the area to be monitored. When the precipitation ≤ 10mm, the maximum allowable error is ±0.4mm, and when the precipitation > 10mm, the maximum allowable error is ±4%, and synchronously collect the real-time precipitation intensity and cumulative rainfall.

[0197] The landslide monitoring basic information database adopts a multi-source heterogeneous data storage architecture, including the following interrelated sub-databases:

[0198] The meteorological data sub-database stores meteorological data including precipitation observation historical data and intelligent grid weather forecast precipitation data in the past N whole decades in the administrative region where it is located.

[0199] The geographical information data sub-library stores geographical data including the elevation, slope, and land use data of the administrative region where it is located.

[0200] The geological environment data sub-library stores geological environment data including the stratigraphic lithology and fault density data of the administrative region where it is located.

[0201] The social and economic data sub-library stores social and economic data including the gross domestic product and population density data of the administrative region where it is located.

[0202] The landslide monitoring data sub-library stores relevant monitoring data including the deformation monitoring data of the landslide surface scene, precipitation observation data, and the performance parameters and operating status of automated monitoring equipment during the monitoring period of the area to be monitored.

[0203] The early warning message data sub-library stores early warning information including geographical location information, landslide risk analysis, precipitation trend analysis, and emergency measures. Specifically, it structurally stores the early warning geographical fence coordinates (WGS84 format), risk probability matrix, emergency response plan text, and multimedia guidelines.

[0204] It should be emphasized that the intelligent regulation and management platform includes the following modules: the meteorological data analysis module, which is used to analyze and process intelligent grid forecasts and actual rainfall data; the intelligent regulation module for automated monitoring equipment, which is used to intelligently control the monitoring status, data acquisition frequency, data transmission, etc. of the monitoring equipment of the landslide automated monitoring station; the data management module, which is used to create, store, update, etc. the data management of the landslide monitoring basic information database system; the landslide data analysis and display module, which is used to comprehensively calculate, tabulate, and graphically display the intelligent grid forecast precipitation data, landslide surface scene deformation monitoring data, and precipitation observation data; the early warning information release module, which is used to release landslide early warning information.

[0205] In addition, the system also includes: a display terminal, which is communicatively connected to the intelligent regulation and management platform, and is used for logging in to the landslide monitoring management platform and locally displaying data including landslide monitoring data, precipitation forecasts, and the precipitation-landslide meteorological risk analysis results.

[0206] In summary, the embodiment of the present invention provides a method and system for intelligent monitoring of landslide surface scene deformation based on precipitation forecasts. The present invention uses automated landslide surface scene deformation monitoring equipment and automated precipitation monitoring equipment as hardware support, and through the technical integration of computer and communication technologies, intelligent grid precipitation forecast technologies, etc., constructs a method and system for intelligent monitoring of landslide surface scene deformation based on precipitation forecasts, realizes the intelligent interactive control of the precipitation-induced landslide surface scene deformation monitoring process, improves the overall efficiency of landslide monitoring, and reduces the consumption of manpower, material resources, and resources.

[0207] Since the system / apparatus described in the above embodiments of the present invention is the system / apparatus adopted for implementing the method in the above embodiments of the present invention, those skilled in the art can understand the specific structure and variations of the system / apparatus based on the method described in the above embodiments of the present invention, and thus will not be elaborated herein. Any system / apparatus adopted for the method in the above embodiments of the present invention falls within the scope of protection of the present invention.

[0208] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0209] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions.

[0210] It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of components or steps not listed in the claim. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present invention can be implemented by means of hardware including several different components and by means of a suitably programmed computer. In a claim listing several devices, several of these devices can be embodied by the same piece of hardware. The use of the words first, second, third, etc. is only for convenience of expression and does not denote any order. These words can be understood as part of the component name.

[0211] In addition, it should be noted that in the description of this specification, the description of terms such as "an embodiment", "some embodiments", "embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0212] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications after learning the basic creative concept. Therefore, the claims should be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0213] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention should also cover these modifications and variations.

Claims

1. An intelligent monitoring method for landslide surface deformation based on precipitation forecast, characterized in that: include: According to the historical precipitation data of the monitored area, the landslide precipitation risk level is determined by coupling the hazard index of the hazard factor, the hazard index of the disaster-prone environment and the vulnerability index of the hazard-bearing body; Adaptively select and implement a landslide surface scene deformation monitoring scheme including one-dimensional deformation vector monitoring and / or three-dimensional deformation vector monitoring based on the landslide precipitation risk level; The precipitation intensity is calculated based on the obtained intelligent grid forecast data, and combined with the landslide precipitation risk level, a risk level-precipitation intensity two-dimensional control matrix is ​​established. Based on the two-dimensional control matrix mechanism, the risk level and precipitation intensity are cross-mapped into control parameters in real time to achieve tracking and dynamic adjustment of the deformation monitoring frequency and precipitation monitoring frequency of the landslide surface scene; A precipitation-landslide meteorological risk index model is constructed based on the spatiotemporal coupling relationship between landslide deformation and accumulated rainfall. When the risk index value exceeds the preset risk threshold, an early warning message containing geographic location information, landslide risk analysis, precipitation trend analysis and emergency measures is generated and issued.

2. The intelligent monitoring method for landslide surface deformation based on precipitation forecast as claimed in claim 1, characterized in that: Based on the historical precipitation data of the monitored area, the landslide precipitation risk level is determined by coupling the hazard index of the hazard factor, the hazard index of the disaster-prone environment and the vulnerability index of the hazard-bearing body, including: Obtain the historical meteorological observation data of the past N whole decades from the meteorological stations near the monitored area, and obtain the four-dimensional characteristic data including the maximum hourly rainfall of the rainstorm process, the maximum daily rainfall of the rainstorm process, the accumulated rainfall of the rainstorm process, and the number of days of the rainstorm from the historical meteorological observation data; The four-dimensional characteristic data is normalized, and the weight coefficients of each four-dimensional characteristic data are dynamically determined by the information entropy weighting method, and the disaster factor hazard index of the area to be monitored is obtained through the pre-constructed disaster factor hazard index formula; The terrain relief of the unit to be monitored is obtained by using the difference method of the altitude of adjacent units. Combined with the slope and fault density of the unit to be monitored, after normalization, the disaster-prone environment hazard index of the area to be monitored is obtained through the pre-constructed disaster-prone environment hazard index formula. According to the obtained gross domestic product, population density and land use type of the unit where the monitored area is located, the weight coefficients of gross domestic product, population density and land use type are dynamically determined by using the information entropy weighting method, and the vulnerability index of the hazard-prone body is obtained through the pre-constructed hazard-prone body vulnerability index formula; Based on the hazard index of hazard factors, the hazard index of disaster-prone environment and the vulnerability index of disaster-prone body, a multi-dimensional risk coupling calculation formula of hazard factors-disaster-prone environment-disaster-prone body is established, and the landslide precipitation risk degree of the monitored area is calculated; Based on the percentile classification of historical disaster statistics, a quantitative correspondence between landslide precipitation risk and risk level is established; in, The formula for the hazard index of disaster factors is: In the formula, I is the hazard index of the disaster factor, I 1max is the normalized value of the maximum hourly rainfall in the rainstorm process, I 24max Normalized value of maximum daily rainfall during rainstorm, I prc I is the normalized value of the accumulated rainfall during the rainstorm process; day is the normalized value of the duration of the rainstorm process, n is the number of rainstorm processes in the past N whole decades, a, b, c, d are weight coefficients; The formula for the disaster-prone environment hazard index is: P = w1×E+w2×S+w3×F; Where, E is the terrain undulation of the unit where the monitored area is located, S is the terrain slope of the unit where the monitored area is located, F is the fault density of the unit where the monitored area is located, and w1, w2, and w3 are the weight coefficients of the disaster-prone environmental factors; The terrain relief is: In the formula, h i is the altitude of the neighboring unit of the unit where the monitored area is located, is the altitude of the unit where the monitored area is located, and n0 is the number of neighboring units of the unit where the monitored area is located; The formula of the disaster-bearing body vulnerability index is: V = p1 × G + p2 × D + p3 × L; Where V is the vulnerability index of the hazard-prone body, G is the normalized value of the gross domestic product of the unit where the monitored area is located, D is the normalized value of the population density of the unit where the monitored area is located, L is the land use type of the unit where the monitored area is located, and p1, p2, and p3 are the weight coefficients of the hazard-prone body factors. The multi-dimensional risk coupling calculation formula of disaster-causing factors-disaster-pregnant environment-disaster-bearing body is: R = I × P × V; Where R is the landslide precipitation risk; When the landslide precipitation risk percentile is >95%, the landslide precipitation risk level is the fourth risk level; when the landslide precipitation risk percentile is >80% and ≤95%, the landslide precipitation risk level is the third risk level; when the landslide precipitation risk percentile is >50% and ≤80%, the landslide precipitation risk level is the second risk level; when the landslide precipitation risk percentile is ≤50%, the landslide precipitation risk level is the first risk level.

3. The intelligent monitoring method for landslide surface deformation based on precipitation forecast as claimed in claim 1, characterized in that: The landslide surface scene deformation monitoring scheme including one-dimensional deformation vector monitoring and / or three-dimensional deformation vector monitoring is adaptively selected and implemented based on the landslide precipitation risk level, including: Establish risk level-monitoring mode mapping rules, and filter the determined risk levels to obtain corresponding monitoring modes according to the risk level-monitoring mode mapping rules; According to the determined monitoring mode, a selection matrix including detection dimension, field of view angle threshold, maximum detection distance equipment and anti-electromagnetic interference level is established; Based on the equipment selection matrix and risk level, dynamically match equipment parameter combinations, and automatically trigger equipment performance adjustment conditions when the risk level increases to update the latest equipment selection plan; According to the landslide area of ​​the area to be monitored, the maximum detection distance of the equipment and the field of view of the equipment, combined with the introduced electromagnetic interference density compensation coefficient, the minimum number of monitoring stations is obtained; Based on the obtained minimum monitoring station data and the distribution of electromagnetic interference sources, the visibility factor is introduced to generate the optimal deployment topology; According to the acquired point cloud data, the radar beam obstruction area is identified, and the terrain obstruction index is obtained by dividing the area of ​​the obstruction area by the projected area of ​​the landslide area. When the terrain obstruction index is less than 0.3, the compensation mechanism is triggered: additional movable devices are added in the obstruction area to form redundant coverage with a monitoring radius overlap of ≥20%; the scanning frequency of adjacent nodes is dynamically increased to 1.5 times the baseline value; When deploying precipitation monitoring stations in the area to be monitored, perform local rainfall correlation analysis, extract historical rainfall data of the monitoring stations and the landslide center point during the same period, and calculate the Pearson correlation coefficient ρ between the rainfall at the monitoring station and the landslide center point; When ρ<0.95, the site relocation is triggered, and the optimization objective function of minimizing the absolute error of historical rainfall and the constraint condition that the distance between the site and the landslide edge is ≤1.2 times the landslide diameter is constructed. The center of the landslide is taken as the origin, and candidate grids with a radius of ≤1km are generated. Each candidate grid is traversed, and candidate grid points that meet the minimum value of the absolute error of historical rainfall and the distance from the landslide edge is ≤1.2 times the landslide diameter are selected to obtain the optimal layout coordinates. After the precipitation monitoring station is deployed, the correlation coefficient change is monitored in real time. If the Pearson correlation coefficient ρ of N consecutive rainfall events decreases by more than 5%, the station relocation step is triggered again; in, The risk level-monitoring mode mapping rule is as follows: when the risk level is the first risk level, the one-dimensional deformation vector monitoring mode implemented by the space-borne SAR or slope radar is adopted; when the risk level is the second risk level, the one-dimensional deformation vector monitoring mode is superimposed on the intermittent three-dimensional deformation vector monitoring mode implemented by the Beidou radar; when the risk level is the third risk level, the three-dimensional deformation vector monitoring mode implemented by the Beidou radar is superimposed on the intermittent one-dimensional deformation vector monitoring mode; when the risk level is the fourth risk level, the dual-mode full-band collaborative monitoring mode of the one-dimensional deformation vector monitoring mode and the three-dimensional deformation vector monitoring mode is adopted, and the dual-mode data acquisition time synchronization error is ≤1 second; The conditions for equipment performance upgrade are: Where D current is the actual detection distance of the current equipment, L is the quantitative value of the current landslide risk level, 1 to 4 correspond to the first risk level to the fourth risk level, L0 is the base risk level, and the base risk level value is equal to the first risk level quantitative value, D base is the basic detection distance, which is the length of the major axis of the landslide area × 1.2; The minimum monitoring station data is: Where S is the projected area of ​​the landslide, D is the maximum detection distance of the equipment, θ is the field of view, α is the electromagnetic interference density compensation coefficient, and EMI d is the electromagnetic interference density index, n r is the electromagnetic interference source data, r j is the average radiated power.

4. The intelligent monitoring method for landslide surface deformation based on precipitation forecast as claimed in claim 1, characterized in that: The precipitation intensity is calculated based on the obtained intelligent grid forecast data, and combined with the landslide precipitation risk level, a risk level-precipitation intensity two-dimensional control matrix is ​​established. Based on the two-dimensional control matrix mechanism, the risk level and precipitation intensity are cross-mapped into control parameters in real time to achieve tracking and dynamic adjustment of the deformation monitoring frequency and precipitation monitoring frequency of the landslide surface scene, including: Construct a two-dimensional control matrix of landslide precipitation risk level and precipitation intensity, set the landslide precipitation risk level as the horizontal control parameter of the matrix, and the precipitation intensity level as the vertical control parameter; Preset the landslide scene deformation monitoring frequency threshold and precipitation monitoring frequency threshold at each cross node of the two-dimensional control matrix to form a standardized control parameter mapping rule; Obtain the intelligent grid precipitation forecast data of the landslide area in real time, calculate the precipitation intensity index based on the intelligent grid precipitation forecast data and divide the dynamic precipitation intensity level; The dynamic precipitation intensity level and landslide precipitation risk level of the monitored area are input into the two-dimensional control matrix for cross-mapping and matching, and the adapted deformation monitoring frequency control parameters and precipitation monitoring frequency control parameters are output; Remotely configure the working mode of the automated monitoring equipment according to the control parameters, perform one-dimensional / three-dimensional deformation vector acquisition according to the deformation monitoring frequency threshold of the landslide scene, and keep the sampling frequency of the rainfall monitoring equipment no lower than the deformation monitoring frequency and dynamically match the changes in precipitation intensity; Establish a multi-dimensional data linkage trigger mechanism to trigger the recalibration of control parameters when the following situations occur: the actual precipitation intensity exceeds the forecast level threshold, the deformation rate exceeds the preset safety threshold, and the number of days of heavy rain process reaches the preset critical value of the matrix; After the precipitation process ends, the slope reduction control is initiated, the monitoring frequency is reduced according to the gradient, and the weights of historical event parameters in the two-dimensional control matrix are updated synchronously.

5. The intelligent monitoring method for landslide surface deformation based on precipitation forecast as claimed in claim 4, characterized in that: Real-time acquisition of intelligent grid precipitation forecast data in the area where the landslide is located, calculation of precipitation intensity index based on intelligent grid precipitation forecast data and classification of dynamic precipitation intensity levels include: Obtain intelligent grid forecast data, extract meteorological element information including the start time, end time, and hourly rainfall of future precipitation processes according to the longitude and latitude information of the center of the landslide area, and calculate the maximum hourly rainfall, maximum daily rainfall, accumulated rainfall, and duration of the rainstorm process based on the meteorological element information; According to the normalized values ​​of the maximum hourly rainfall, maximum daily rainfall, accumulated rainfall and the duration of the rainstorm process, combined with the number of rainstorm processes in the past N whole years and the corresponding weight coefficients, the precipitation intensity index of the future precipitation process is calculated; The historical meteorological observation data of nearly N whole decades from the meteorological stations near the monitored area are used to calculate the precipitation intensity index of all previous rainstorm processes and establish a precipitation intensity classification table containing multiple levels.

6. The intelligent monitoring method for landslide surface deformation based on precipitation forecast as claimed in claim 1, characterized in that: Based on the spatiotemporal coupling relationship between landslide deformation and accumulated rainfall, a precipitation-landslide meteorological risk index model is constructed. When the risk index value exceeds the preset risk threshold, an early warning message containing geographic location information, landslide risk analysis, precipitation trend analysis and emergency measures is generated and issued, including: In the landslide initiation phase, the benchmark calculation units are divided based on the sampling frequency of the monitoring equipment; The three-dimensional deformation vector increments of the landslide surface and the corresponding accumulated rainfall in each period are synchronously collected and aligned to form a spatiotemporal coupled data set; According to the benchmark calculation unit, a nonlinear weighted algorithm is used to calculate the precipitation-landslide meteorological risk index. At the same time, when the deformation rate exceeds the benchmark value by 30%, the time period length of the benchmark calculation unit is automatically shortened; According to the results of cluster analysis of historical disaster data, differentiated risk threshold intervals are set; When the real-time calculated precipitation-landslide meteorological risk index exceeds the corresponding risk threshold interval, the three-dimensional geographic coordinates and impact radius of the landslide body, the real-time deformation field contour distribution map, the next 72 hours precipitation intelligent grid forecast product and the emergency plan are automatically associated from at least one data source and packaged into a structured early warning message; The structured early warning message is pushed synchronously to the preset processing entity through satellite communication channels, 5G emergency broadcasts and Internet of Things terminals.

7. A landslide surface scene deformation intelligent control monitoring system based on precipitation forecast, characterized in that: include: A cluster of automated landslide monitoring stations, including one-dimensional deformation monitoring stations, three-dimensional deformation monitoring stations, and rainfall monitoring stations; The landslide monitoring basic information database is used to store the original data, intermediate data and analysis result data related to the integrated landslide surface scene deformation monitoring system, including meteorological data, geographic information data, geological environment data, socio-economic data, landslide monitoring data, early warning information data, intelligent grid forecast data, rainfall history data, precipitation monitoring data, landslide deformation monitoring data and equipment information; and An intelligent control and management platform is used to execute the method described in any one of claims 1 to 6, and the intelligent control and management platform also interacts with the meteorological department's intelligent grid forecasting system.

8. The landslide surface scene deformation intelligently controllable monitoring system based on precipitation forecast according to claim 7 is characterized in that: One-dimensional deformation monitoring stations are equipped with spaceborne SAR or slope radar; The three-dimensional deformation monitoring station is equipped with Beidou surface scene radar; Rainfall monitoring stations are set up within 1km of the area to be monitored.

9. The landslide surface scene deformation intelligent control monitoring system based on precipitation forecast according to claim 7 is characterized in that: The landslide monitoring basic information database contains the following interrelated sub-databases: The meteorological data sub-database stores meteorological data including historical precipitation observation data for the past N decades and precipitation data from smart grid weather forecasts in the administrative area; The geographic information data sub-database stores geographic data including altitude, slope, and land use data of the administrative area; The geological environment data sub-database stores address environment data including stratum lithology and fault density data of the administrative area; The socio-economic data sub-database stores socio-economic data including GDP and population density data of the administrative area; The landslide monitoring data sub-database stores the monitoring data including the deformation monitoring data of the landslide surface during the monitoring period, the precipitation observation data, and the performance parameters and operation status of the automated monitoring equipment; The early warning message data sub-database stores early warning information including geographic location information, landslide risk analysis, precipitation trend analysis and emergency measures.

10. The intelligently controllable monitoring system for landslide surface scene deformation based on precipitation forecast according to any one of claims 7 to 9, characterized in that: include: The display terminal is connected to the intelligent control and management platform for logging into the landslide monitoring and management platform, and for local display of data including landslide monitoring data, precipitation forecast and precipitation-landslide meteorological risk analysis results.

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